Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Product Overview

This algorithm produces similarity scores for a document or a line of text compared to documents in a corpus. The algorithm includes a tf-idf text featurizer to create n-gram features describing the text. It then uses the library scipy.spatial.distance to compute the cosine distance between the new document and each one in the corpus based on all n-gram features in the texts. The similarity index is then computed as (1 - cosine_distance). You can use this algorithm to look for similar texts and detect plagiarism in documents.

Highlights

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Usage Information

Fulfillment Methods

Amazon SageMaker

This algorithm requires a csv with column names, 'review_id' (String) and 'Text_clean' (String).
'Text_clean' contains the lines of text or documents in a corpus. Each line should be denoted by a 'review_id', while the new text, or line that is wanting to be analyzed should contain the keyword, 'NEW_REVIEW' as its 'review_id'
See notebook for usage details.

End User License Agreement

Additional Resources

Amazon SageMaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models. For additional information on Amazon SageMaker, please click Learn more

Support Information

AWS Infrastructure

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